Genotype analysis

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Last updated: 2023-02-14

Checks: 6 1

Knit directory: scRNA_Qualitycheck/

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Unstaged changes:
    Modified:   analysis/01_QC_scRNA.Rmd
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    Modified:   analysis/04_GenotypeAnalysis_scRNA.Rmd
    Deleted:    output/README.md

Staged changes:
    Modified:   analysis/01_QC_scRNA.Rmd
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File	Version	Author	Date	Message
Rmd	834af32	achiocch	2023-02-13	full analysis
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Rmd	d0a0fec	achiocch	2023-02-12	wflow_publish(c("./analysis/", "./docs/", "./code/*"))
Rmd	e16a087	achiocch	2023-02-12	initial commit MVP Analysis
html	e16a087	achiocch	2023-02-12	initial commit MVP Analysis

---

recalc=F
if(recalc){
  load(file = "./output/03_Trajectory_scRNA_seurat_sling.Rdata")}
if(!recalc){
  load(file = "./output/04_GenotypeAnalysis_scRNA_Output.Rdata")
}

  coldata = seurat_integrated@meta.data
  countdata = as.matrix(seurat_integrated@assays$SCT@scale.data)
  coldata$TR = gsub(".*(TR.)", "\\1", coldata$SampleID)
  
  model = as.formula("x ~ Genotype+(1|TR)+(1|BatchID)")
  
if(recalc){  
  runlmer = function(i, mod, gdata, cdata){
    #cat(i, end="\r")
    cdata$x=gdata[i,]
    res = suppressMessages(lmer(mod, data=cdata, verbose = 0))
    res = summary(res)
    return(res$coefficients[2,])
  }
  
  reslmer <- lapply(1:nrow(countdata), function(i){
    runlmer(i, gdata = countdata, mod = model, cdata = coldata)})
  names(reslmer) = rownames(countdata)
  reslmer <- do.call(rbind, reslmer)
  reslmer <- as.data.frame(reslmer)
  reslmer$p.value = 2*pt(abs(reslmer$`t value`), df=ncol(countdata)-1, lower=FALSE)
  reslmer$adj.p = p.adjust(reslmer$p, method="BH")
}

ggplot(reslmer, aes(x=Estimate, y=-log10(p.value), col=(adj.p<0.05)))+
  geom_point()

if(recalc){
  Idents(seurat_integrated) = "Genotype"
  
  Genotype.Markers= FindMarkers(seurat_integrated, 
                                assay = "integrated",
                                test.use = "wilcox",
                                ident.1 = "C/C", only.pos = F)
  
  Genotype.Markers <- Genotype.Markers[Genotype.Markers$p_val_adj<0.05,]
  Genotype.Markers %>%
    top_n(n = 20, wt = abs(avg_log2FC)) -> top10Genotype.Markers
  
  Genotype.Markers = Genotype.Markers[order(Genotype.Markers$avg_log2FC),]
}

DoHeatmap(seurat_integrated, features = rownames(top10Genotype.Markers), 
          group.by = "Genotype", assay = "integrated")

#Calculating the percentages of cell per cluster
number_perCluster<- as.data.frame.matrix(table(Samples=seurat_integrated$SampleID, 
                                               Clusters=scRNA.sce$seurat_clusters))
prop_per_Sample<- prop.table(table(Samples=scRNA.sce$Sample, 
                                   Clusters=scRNA.sce$seurat_clusters), margin = 1)

SampleData = unique(coldata[,c("SampleID", "BatchID", "Genotype", "TR")])


res = apply(prop_per_Sample, 2, function(x){summary(lmer(x~Genotype+(1|TR), data=SampleData))})

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

ResFreq <- lapply(res, function(x) x$coefficients[2,]) %>% do.call(rbind,.) %>% as.data.frame()

ResFreq$pvalue = 2*pt(abs(ResFreq$`t value`), df=10, lower.tail = F)
ResFreq$p.adj = p.adjust(ResFreq$pvalue)

DT::datatable(ResFreq, extensions = "Buttons",
              filter="top",
              caption = paste0("Effects of Genotype on cluster"),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              ))

gene based cluster based

if(recalc){
  reslist.by.cluster=list()
  for(i in unique(seurat_integrated$seurat_clusters)){
    #print(i)
    cellidx=coldata$seurat_clusters==i
    countdata.subset=countdata[,cellidx]
    reslmer.cluster <- lapply(1:nrow(countdata), function(i){
      runlmer(i, gdata = countdata.subset, mod = model, cdata = coldata[cellidx,])})
    reslmer.cluster <- do.call(rbind, reslmer.cluster)
    reslmer.cluster <- as.data.frame(reslmer.cluster)
    reslmer.cluster$p.value = 2*pt(abs(reslmer.cluster$`t value`), df=ncol(countdata)-1, lower=FALSE)
    reslmer.cluster$adj.p = p.adjust(reslmer.cluster$p, method="BH")
    row.names(reslmer.cluster) = rownames(countdata)
    reslist.by.cluster[[i]] = reslmer.cluster
    
  }

  
names(reslist.by.cluster) <- unique(seurat_integrated$seurat_clusters)

reslist.by.cluster = reslist.by.cluster[as.character(sort(unique(seurat_integrated$seurat_clusters)))]

DEX.reslist.by.cluster = lapply(reslist.by.cluster, function(x){rownames(x)[x$adj.p<=0.05 & 
                                                                              x$Estimate>=0.2]})

names(DEX.reslist.by.cluster)  <- names(reslist.by.cluster) 

Gost.reslist.by.cluster = lapply(DEX.reslist.by.cluster, function(x){gost(x, 
                                                                          evcodes = T)})

names(Gost.reslist.by.cluster)  <- names(reslist.by.cluster) 

}

Results Cluster 0

x="0"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 1

x="1"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 2

x="2"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 3

x="3"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 4

x="4"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 5

x="5"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 6

x="6"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 7

x="7"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 8

x="8"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 9

x="9"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 10

x="10"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 11

x="11"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 12

x="12"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 13

x="13"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

Results Cluster 14

x="14"
df = reslist.by.cluster[[x]]
df = df[order(df$adj.p, decreasing = F),]
DT::datatable(df, extensions = "Buttons",
              filter="top",
              caption = paste0("Genewise results cluster ",x),
              options = list(
                pageLength = 10,
                info = FALSE,
                lengthMenu = list(c(15,50, 100, -1),
                                  c("15","50", "100" ,"All")
                ),dom = 'Blfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf')
              )
)

ggplot(df, aes(x=Estimate, y=-log10(p.value), color=(adj.p<0.05)))+
      geom_point()+theme_classic()

gostplot(Gost.reslist.by.cluster[[x]])

save(reslist.by.cluster, DEX.reslist.by.cluster, top10Genotype.Markers, Gost.reslist.by.cluster, ResFreq, Genotype.Markers, reslmer, seurat_integrated, scRNA.sce, Cellpalette, SampleGPalette, SamplePalette, prop_per_Sample, file = "./output/04_GenotypeAnalysis_scRNA_Output.Rdata")

Session information

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] lme4_1.1-31                 Matrix_1.5-3               
 [3] gprofiler2_0.2.1            viridis_0.6.2              
 [5] viridisLite_0.4.1           cowplot_1.1.1              
 [7] randomcoloR_1.1.0.1         RCurl_1.98-1.10            
 [9] RColorBrewer_1.1-3          data.table_1.14.6          
[11] lubridate_1.9.1             forcats_1.0.0              
[13] stringr_1.5.0               dplyr_1.1.0                
[15] purrr_1.0.1                 readr_2.1.3                
[17] tidyr_1.3.0                 tibble_3.1.8               
[19] tidyverse_1.3.2             scater_1.26.1              
[21] org.Hs.eg.db_3.16.0         AnnotationDbi_1.60.0       
[23] AnnotationHub_3.6.0         BiocFileCache_2.6.0        
[25] dbplyr_2.3.0                celldex_1.8.0              
[27] scuttle_1.8.4               ggplot2_3.4.0              
[29] slingshot_2.6.0             TrajectoryUtils_1.6.0      
[31] princurve_2.1.6             SeuratObject_4.1.3         
[33] Seurat_4.3.0                DropletUtils_1.18.1        
[35] SingleCellExperiment_1.20.0 SingleR_2.0.0              
[37] SummarizedExperiment_1.28.0 Biobase_2.58.0             
[39] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
[41] IRanges_2.32.0              S4Vectors_0.36.1           
[43] BiocGenerics_0.44.0         MatrixGenerics_1.10.0      
[45] matrixStats_0.63.0         

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.3                scattermore_0.8              
  [3] R.methodsS3_1.8.2             bit64_4.0.5                  
  [5] knitr_1.42                    irlba_2.3.5.1                
  [7] DelayedArray_0.24.0           R.utils_2.12.2               
  [9] KEGGREST_1.38.0               generics_0.1.3               
 [11] ScaledMatrix_1.6.0            RSQLite_2.2.20               
 [13] RANN_2.6.1                    future_1.31.0                
 [15] tzdb_0.3.0                    bit_4.0.5                    
 [17] xml2_1.3.3                    spatstat.data_3.0-0          
 [19] httpuv_1.6.8                  assertthat_0.2.1             
 [21] gargle_1.3.0                  xfun_0.37                    
 [23] hms_1.1.2                     jquerylib_0.1.4              
 [25] evaluate_0.20                 promises_1.2.0.1             
 [27] fansi_1.0.4                   readxl_1.4.1                 
 [29] igraph_1.3.5                  DBI_1.1.3                    
 [31] htmlwidgets_1.6.1             spatstat.geom_3.0-6          
 [33] googledrive_2.0.0             ellipsis_0.3.2               
 [35] crosstalk_1.2.0               backports_1.4.1              
 [37] V8_4.2.2                      deldir_1.0-6                 
 [39] sparseMatrixStats_1.10.0      vctrs_0.5.2                  
 [41] ROCR_1.0-11                   abind_1.4-5                  
 [43] cachem_1.0.6                  withr_2.5.0                  
 [45] progressr_0.13.0              sctransform_0.3.5            
 [47] goftest_1.2-3                 cluster_2.1.4                
 [49] ExperimentHub_2.6.0           lazyeval_0.2.2               
 [51] crayon_1.5.2                  spatstat.explore_3.0-6       
 [53] labeling_0.4.2                edgeR_3.40.2                 
 [55] pkgconfig_2.0.3               nlme_3.1-162                 
 [57] vipor_0.4.5                   rlang_1.0.6                  
 [59] globals_0.16.2                lifecycle_1.0.3              
 [61] miniUI_0.1.1.1                filelock_1.0.2               
 [63] modelr_0.1.10                 rsvd_1.0.5                   
 [65] cellranger_1.1.0              rprojroot_2.0.3              
 [67] polyclip_1.10-4               lmtest_0.9-40                
 [69] boot_1.3-28.1                 Rhdf5lib_1.20.0              
 [71] zoo_1.8-11                    reprex_2.0.2                 
 [73] beeswarm_0.4.0                whisker_0.4.1                
 [75] ggridges_0.5.4                googlesheets4_1.0.1          
 [77] png_0.1-8                     bitops_1.0-7                 
 [79] R.oo_1.25.0                   KernSmooth_2.23-20           
 [81] rhdf5filters_1.10.0           Biostrings_2.66.0            
 [83] blob_1.2.3                    DelayedMatrixStats_1.20.0    
 [85] workflowr_1.7.0               parallelly_1.34.0            
 [87] spatstat.random_3.1-3         beachmat_2.14.0              
 [89] scales_1.2.1                  memoise_2.0.1                
 [91] magrittr_2.0.3                plyr_1.8.8                   
 [93] ica_1.0-3                     zlibbioc_1.44.0              
 [95] compiler_4.2.2                dqrng_0.3.0                  
 [97] fitdistrplus_1.1-8            cli_3.6.0                    
 [99] XVector_0.38.0                listenv_0.9.0                
[101] patchwork_1.1.2               pbapply_1.7-0                
[103] MASS_7.3-58.2                 tidyselect_1.2.0             
[105] stringi_1.7.12                highr_0.10                   
[107] yaml_2.3.7                    BiocSingular_1.14.0          
[109] locfit_1.5-9.7                ggrepel_0.9.3                
[111] grid_4.2.2                    sass_0.4.5                   
[113] timechange_0.2.0              tools_4.2.2                  
[115] future.apply_1.10.0           parallel_4.2.2               
[117] rstudioapi_0.14               git2r_0.31.0                 
[119] gridExtra_2.3                 farver_2.1.1                 
[121] Rtsne_0.16                    digest_0.6.31                
[123] BiocManager_1.30.19           shiny_1.7.4                  
[125] Rcpp_1.0.10                   broom_1.0.3                  
[127] BiocVersion_3.16.0            later_1.3.0                  
[129] RcppAnnoy_0.0.20              httr_1.4.4                   
[131] colorspace_2.1-0              rvest_1.0.3                  
[133] fs_1.6.1                      tensor_1.5                   
[135] reticulate_1.28               splines_4.2.2                
[137] uwot_0.1.14                   spatstat.utils_3.0-1         
[139] sp_1.6-0                      plotly_4.10.1                
[141] xtable_1.8-4                  nloptr_2.0.3                 
[143] jsonlite_1.8.4                R6_2.5.1                     
[145] pillar_1.8.1                  htmltools_0.5.4              
[147] mime_0.12                     DT_0.27                      
[149] minqa_1.2.5                   glue_1.6.2                   
[151] fastmap_1.1.0                 BiocParallel_1.32.5          
[153] BiocNeighbors_1.16.0          interactiveDisplayBase_1.36.0
[155] codetools_0.2-19              utf8_1.2.3                   
[157] lattice_0.20-45               bslib_0.4.2                  
[159] spatstat.sparse_3.0-0         curl_5.0.0                   
[161] ggbeeswarm_0.7.1              leiden_0.4.3                 
[163] survival_3.5-0                limma_3.54.1                 
[165] rmarkdown_2.20                munsell_0.5.0                
[167] rhdf5_2.42.0                  GenomeInfoDbData_1.2.9       
[169] HDF5Array_1.26.0              haven_2.5.1                  
[171] reshape2_1.4.4                gtable_0.3.1